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Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.


Data Flow Graph of the Viola-Jones recursive equations for a single row of the input image.
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sensors-15-16804-f002: Data Flow Graph of the Viola-Jones recursive equations for a single row of the input image.

Mentions: Equations (6) and (7) represent a two-stage system which operates in a serial fashion: the first stage computes the cumulative row sum at a specific image location and forwards the data to the second stage for calculation of the integral image value at that particular location. The data flow graph of this serial system is shown in Figure 2 for a single row of the input image. It can be observed from Figure 2 that individual stages are also dependent upon data from previous iterations for their operation. The first stage requires the cumulative row sum to be computed in a serial way for a single row of the input image. The second stage is more complex as it needs data from the previous row to calculate an integral image value. Hence, there is little opportunity for parallel computation in single row operations.


Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

Ehsan S, Clark AF - Sensors (Basel) (2015)

Data Flow Graph of the Viola-Jones recursive equations for a single row of the input image.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541907&req=5

sensors-15-16804-f002: Data Flow Graph of the Viola-Jones recursive equations for a single row of the input image.
Mentions: Equations (6) and (7) represent a two-stage system which operates in a serial fashion: the first stage computes the cumulative row sum at a specific image location and forwards the data to the second stage for calculation of the integral image value at that particular location. The data flow graph of this serial system is shown in Figure 2 for a single row of the input image. It can be observed from Figure 2 that individual stages are also dependent upon data from previous iterations for their operation. The first stage requires the cumulative row sum to be computed in a serial way for a single row of the input image. The second stage is more complex as it needs data from the previous row to calculate an integral image value. Hence, there is little opportunity for parallel computation in single row operations.

Bottom Line: The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size.An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video).Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK. sehsan@essex.ac.uk.

ABSTRACT
The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

No MeSH data available.